In dentistry, 3D scanning and imaging are rapidly replacing older techniques that use castings and impression materials. Scanning is typically fast relative to the older methods, can instantly provide a digital file, and can eliminate substantially all shrinkage and handling issues associated with castings and impressions. Additionally, the digital images can be easily transmitted to a dental laboratory or dental computerized numerical control (CNC) milling machine, for generating a suitable dental restoration component such as a dental crown.
Scanners, in general, are devices for capturing and recording information from the surface of an object. The use of scanners to determine a 3D surface contour of an object, e.g., to create a 3D model thereof, using non-contact optical methods is important in many applications including in vivo scanning of dental structures. Typically, a 3D surface contour is formed from a collection of points (often called a cloud of points) where, at a particular time, the relative position of each point in the collection/cloud represents an approximate contour of the scanned object's surface.
In these optical methods, a common principle underlying contour measurement using the collection of point position data is triangulation. Given one or more triangles where the baseline of each triangle includes two optical centers and the vertex of each triangle is a particular point on or near a target object surface, the range of that particular point on or near the target object surface from each of the optical centers can be determined based on the optical separation and the angle of light transmitted from and/or received at the optical centers to/from the particular point. If the coordinate positions of the optical centers in a specified coordinate reference frame (e.g., a Cartesian X, Y, Z reference frame), are known, the relative X, Y, Z coordinate position of the vertex, i.e., the point on or near the target surface, can be computed in the same reference frame. Typically, the light rays from an illumination source to a point on the target form one leg, i.e., edge, of the triangle, and the rays reflected from the target point to an image sensor form the other leg, i.e., edge, of the triangle. In a system using a single image sensor, the angle between the two legs can be determined because the positions of the illumination source and the sensor and the angle at which a beam of illumination light is directed to the surface to be scanned are known. Using these known parameters and the computed angle of reflection, the expected position of the point of reflection on the surface to be contoured can be determined. By repeating this procedure to determine the respective positions of a number of points of reflection a curvature of the reflection surface, i.e., the 3D contour thereof, can be determined.
Triangulation methods can be divided into passive triangulation and active triangulation. Passive triangulation (also known as stereo analysis) typically utilizes ambient light and the two optical centers along the baseline of the triangle include two cameras/image sensors. In two sensor passive systems, knowledge of the angle of illumination light incident upon the object to be scanned is not required. In contrast, active triangulation typically uses one camera as one optical center of the triangle along the baseline and, instead of a second camera at the other optical center, active triangulation uses a source of controlled illumination (also known as structured light). One optical center is a source of light and the other optical center is the imaging device, as described above.
Stereo/passive analysis, while conceptually straightforward, is not widely used, e.g., due to the difficulty in obtaining correspondence between features observed in different camera images. The surface contour of objects with well-defined edges and corners, such as blocks, can be relatively easy to measure using stereo analysis. Objects that have smoothly varying surfaces, such as skin, tooth surfaces, etc., have relatively fewer easily identifiable points of interest, such as corners, edge points, etc. This can present a significant challenge to the stereo analysis techniques. Active triangulation is therefore often preferred in generating 3D contours of such objects having smoothly varying surfaces.
Active triangulation, or structured light methods, can overcome or at least minimize the stereo correspondence problems by projecting one or more known patterns of light onto an object to determine the shape thereof. An example structured light is a spot of light, typically produced by a laser. Accuracy of contour determination can be increased by moving a fine spot in a specified pattern, e.g., along a line, in a zig-zag pattern, and/or a spiral pattern. One large spot can also be used, however. The geometry of the setup of the light projector and the camera observing the spot of light reflected from a surface of the target object can enable, e.g., via trigonometric calculations, the determination of a range of the point from which the light spot is reflected from one or both optical centers (i.e., the light projector and camera), as described above. Light projection patterns such as a stripe or two-dimensional patterns such as a grid of light dots can be used to decrease the time required to capture and/or analyze the images of the target surface.
The resolution of the measurement of the surface of a target object using structured light generally depends on the fineness of the light pattern used and the resolution of the camera used to observe the reflected light. Typically, the overall accuracy of a 3D laser triangulation scanning system is based on the ability thereof to meet two objectives, namely: (i) accurately measuring the center of the illumination light reflected from the target surface, and (ii) accurately measuring the position of the illumination source and the camera at each of the positions used by the scanner to acquire an image.
Commercially available 3D scanner systems have been developed for the dental market that accommodate the variety of human dentitions by incorporating an operator held, wand type scanner. In these systems, the operator typically moves the scanner over the area to be scanned and collects a series of image frames. In this case, however, a positional correspondence between image frames is typically not maintained; instead each frame is captured from an unknown coordinate position that is independent of the position and orientation of the wand at the instant the previous one or more frames of images were captured. In addition, all orientation information about the illumination sources and imaging devices and references thereto from scanning prior to treatment are generally not available to a scan after the treatment, because the scanner cannot be continuously located in the mouth during treatment with other instrumentation used for treatment.
These handheld systems must therefore rely on scene registration or the application of an accurate set of fiducials over the area to be scanned. But, for 3D structures such as teeth, the use of pattern recognition or fiducials for frame registration can be error prone, because tooth surfaces do not always provide sufficient registration features to allow for high accuracy scene registration. Accurate placement of fiducials to a resolution that is often required is generally impractical over the size of a typical tooth.
Another 3D measurement method includes auto-focus depth measurement with image recognition. With a short depth of field, the camera is focused at predefined depth (e.g., Z1), and an image is captured. The image is then processed, e.g., using an image recognition software, so that the “in-focus” sections of the image can be determined. Another image is then captured at a second predefined depth (e.g., Z2), and the “in-focus” sections in the second image are identified. The Z depth positioning, image capture, and image recognition are repeated according to a required resolution. Once all of the images are captured, the individual image slices can be stacked together to create a 3D image of the object.
In connection with scanning and modeling a treatment area, this method often produces 3D scans lacking the required level of accuracy. This is largely because the images are captured before and after the treatment only, and no images are captured during treatment because that requires interchanging treatment and imaging devices, which cause delay in treatment, inconvenience to the patient, and may also pose safety risk to all those involved in the treatment, particularly when lasers are used in the treatment. Therefore, improved systems and methods are need for scanning areas/regions to be treated.